Computational Behavioral Economics

Authored by: Chen Shu-Heng , Ying-Fang Kao , Ragupathy Venkatachalam

Routledge Handbook of Behavioral Economics

Print publication date:  July  2016
Online publication date:  August  2016

Print ISBN: 9781138821149
eBook ISBN: 9781315743479
Adobe ISBN: 9781317589242


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Computational intelligence has been frequently applied to modeling artificial agents in agent-based computational economics. Commonly used applications include reinforcement learning (Chen, 2013), classifier systems (Vriend, 2002), genetic algorithms, genetic programming (Chen, 2002a, b), swarm intelligence (Boyer, Brorsen & Zhang, 2014), and instance-based learning (Pape & Kurtz, 2013). They are considered as alternative toolkits for the classical or Bayesian statistical models in modeling bounded-rationality and adaptive behavior (Sargent, 1993). However, these toolkits, except for reinforcement learning, are not explicitly grounded in psychology. It, therefore, remains to be seen whether these “machines” (artificial agents) are related to the bounded-rational agents as conceived by behavioral economists. Or, alternatively, to what extent can we relate the general principles or practices that are frequently applied in behavioral economics to the designs of these machines?

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